IEEE Trans Neural Netw Learn Syst. 2014 Apr;25(4):751-63. doi: 10.1109/TNNLS.2013.2281065.
With the advances in biochemistry, molecular biology, and neurochemistry there has been impressive progress in understanding the molecular properties of anesthetic agents. However, there has been little focus on how the molecular properties of anesthetic agents lead to the observed macroscopic property that defines the anesthetic state, that is, lack of responsiveness to noxious stimuli. In this paper, we develop a mean field synaptic drive firing rate cortical neuronal model and demonstrate how the induction of general anesthesia can be explained using multistability; the property whereby the solutions of a dynamical system exhibit multiple attracting equilibria under asymptotically slowly changing inputs or system parameters. In particular, we demonstrate multistability in the mean when the system initial conditions or the system coefficients of the neuronal connectivity matrix are random variables. Uncertainty in the system coefficients is captured by representing system uncertain parameters by a multiplicative white noise model wherein stochastic integration is interpreted in the sense of Itô. Modeling a priori system parameter uncertainty using a multiplicative white noise model is motivated by means of the maximum entropy principle of Jaynes and statistical analysis.
随着生物化学、分子生物学和神经化学的进步，人们在理解麻醉剂的分子特性方面取得了令人瞩目的进展。然而，人们很少关注麻醉剂的分子特性如何导致观察到的宏观特性，即对有害刺激缺乏反应。在本文中，我们开发了一个均值场突触驱动发放率皮质神经元模型，并演示了如何使用多稳定性来解释全身麻醉的诱导；多稳定性是指在渐近缓慢变化的输入或系统参数下，动力系统的解表现出多个吸引平衡点的特性。特别是，当系统初始条件或神经元连接矩阵的系统系数是随机变量时，我们在均值上演示了多稳定性。通过用乘性白噪声模型来表示系统不确定参数，系统系数的不确定性得到了捕捉，其中随机积分的解释是伊托意义上的。使用乘性白噪声模型对系统先验参数不确定性进行建模的动机是基于杰恩斯的最大熵原理和统计分析。
